Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders
Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh
Abstract
The Variational Auto-Encoder (VAE) is a popular method for learning a generative model and embeddings of the data. Many real datasets are hierarchically structured. However, traditional VAEs map data in a Euclidean latent space which cannot efficiently embed tree-like structures. Hyperbolic spaces with negative curvature can. We therefore endow VAEs with a Poincaré ball model of hyperbolic geometry as a latent space and rigorously derive the necessary methods to work with two main Gaussian generalisations on that space. We empirically show better generalisation to unseen data than the Euclidean counterpart, and can qualitatively and quantitatively better recover hierarchical structures.
BibTeX
@inproceedings{NEURIPS2019_0ec04cb3,
author = {Mathieu, Emile and Le Lan, Charline and Maddison, Chris J. and Tomioka, Ryota and Teh, Yee Whye},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Continuous Hierarchical Representations with Poincar\'{e} Variational Auto-Encoders},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/0ec04cb3912c4f08874dd03716f80df1-Paper.pdf},
volume = {32},
year = {2019}
}